Intent recognition optimization processing method, apparatus, and storage medium

ABSTRACT

This application discloses an intent recognition optimization processing method, apparatus, device and storage medium, and relates to the field of internet technology. The implementation scheme of specific method includes: acquiring a first intent set and at least one original corpus; acquiring a first recognition result of each original corpus, where the first recognition result of any one of the original corpus includes a first intent corresponding to the original corpus recognized by the intent recognition model; acquiring a second recognition result of each original corpus, where the second recognition result of any one of the original corpus includes a second intent corresponding to the original corpus obtained through artificial recognition; and performing optimization processing on the first intent set to obtain a second intent set according to the first recognition result and the second recognition result of each original corpus.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to Chinese Patent Application No.202010432368.0, filed on May 20, 2020, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The present application relates to the field of internet technologiesand, in particular, to an intent recognition optimization processingmethod, an apparatus, and a storage medium.

BACKGROUND

With rapid development of internet technologies, the automatic questionand answering service has been widely promoted and applied, where intentrecognition of the acquired corpus is one of the important contentsthereof. In the process of intent recognition of the corpus, it isnecessary to determine and provide an intent set in advance, so that anintent recognition model or an artificial tagger can select intents fromthe intent set for corpus tagging.

In the prior art, after obtaining the intent set by learning the sampledata, the fixed intent set obtained by learning is used for selectingthe intent therein by the intent recognition model or the artificialtagger to perform the corpus tagging.

However, when the classification granularity of the intent set obtainedby learning is too fine or too vague, the recognition efficiency and theaccuracy rate of the corpus intent recognition would be significantlyaffected, which in turn affects the rate and the accuracy of the corpustagging.

SUMMARY

Embodiments of the present application provide an intent recognitionoptimization processing method, an apparatus, and a storage medium,which are used to solve the problem of low intent recognition efficiencyand unguaranteed accuracy caused by too fine or too vague intentclassification.

In a first aspect of the present application, an intent recognitionoptimization processing method is provided, including:

acquiring a first intent set and at least one original corpus;

acquiring a first recognition result of each original corpus, where thefirst recognition result of any one of the original corpus includes afirst intent corresponding to the original corpus recognized by anintent recognition model;

acquiring a second recognition result of each original corpus, where thesecond recognition result of any one of the original corpus includes asecond intent corresponding to the original corpus obtained throughartificial recognition; and

performing optimization processing on the first intent set, according tothe first recognition result and the second recognition result of eachoriginal corpus, to obtain a second intent set.

The optimization of the first intent set is achieved according to themodel recognition result and artificial tagging result of each originalcorpus, i.e. according to the predictive intent and objective intent ofeach original corpus. The practicality of the optimization manner of theintent set is high and the optimization reference factors are diversity,and the recognition efficiency and the recognition accuracy of theintent recognition model can be significantly improved when performingintent recognition using the second intent set obtained through theoptimization.

Further, the performing optimization processing on the first intent set,according to the first recognition result and the second recognitionresult of each original corpus, to obtain a second intent set, includes:

determining, according to the first recognition result and the secondrecognition result of each original corpus, a first corpus number and asecond corpus number corresponding to each intent in the first intentset; and

performing optimization processing on the first intent set, according tothe first corpus number and the second corpus number corresponding toeach intent in the first intent set, to obtain a second intent set; and

where the determining a first corpus number and a second corpus numberof corresponding to each intent in the first intent set, includesperforming following steps for each intent in the first intent set:

determining, according to the first recognition result of each of theoriginal corpus, the number of original corpus that the first intentthereof is the one of the first intent set as the first corpus number;and

determining, according to the second recognition result of each of theoriginal corpus that the first intent thereof is the one of the firstintent set, the number of original corpus that the second intent thereofis the one of the first intent set as the second corpus number.

By means of performing optimization processing on the first intent setaccording to the first corpus number and the second corpus numbercorresponding to each intent in the first intent set, i.e. according tothe number of original corpus corresponding to each intent in the firstintent set that the predictive intent of the original corpus is the oneof the first intent set, and the number of original corpus correspondingto each intent in the first intent set that both the objective intentand the predictive intent are the one of the first intent set, where theoptimization reference factors are diversified and the optimizationeffect is good.

Further, the performing optimization processing on the first intent set,according to the first corpus number and the second corpus numbercorresponding to each intent in the first intent set, to obtain a secondintent set, includes:

determining a first accuracy rate of the first intent set, according tothe first corpus number and the second corpus number corresponding toeach intent in the first intent set;

repeating following steps, until whether every two intents in the firstintent set need to be merged is determined: determining, according tothe first corpus number and the second corpus number corresponding toany two intents in the first intent set, a second accuracy ratecorresponding to a third intent set obtained by merging the two intents,and determining that the two intents need to be merged when the secondaccuracy rate is higher than the first accuracy rate; and

merging any two intents that need to be merged in the first intent set,to obtain the second intent set.

Determining whether any two intents need to be merged according to theaccuracy rates of the first intent set before and after merging the twointents in a first intent set, and thus a second intent set withhigher-precision can be obtained by simplifying the number of intentsand classification of intents in the first intent set, which caneffectively improve the recognition efficiency and recognition accuracyof intent recognition, and is conducive to achieving more efficient andintelligent automation service.

Further, the first accuracy rate is the ratio of a sum of the secondcorpus number corresponding to all intents in the first intent set to asum of the first corpus number corresponding to all intents in the firstintent set; and the second accuracy rate is the ratio of a sum of thesecond corpus number corresponding to all intents in the third intentset to a sum of the first corpus number corresponding to all intents inthe third intent set.

Determining whether this any two intents need to be merged according tothe accuracy rates of the intent sets before and after merging any twointents in the first intent set, can benefits the optimization of theintent set, and thus the technical effect of dual improvements of intentrecognition efficiency and recognition accuracy can be achieved.

Further, the acquiring the second recognition result of each originalcorpus includes:

determining a tagging value of each original corpus according to thefirst recognition result of each original corpus, and determining anoriginal corpus that the tagging value thereof exceeds a set thresholdas a valuable corpus, to obtain at least one valuable corpus; and

acquiring the second recognition result of each valuable corpus.

Determining the valuable corpus according to the first recognitionresult of each original corpus, and acquiring the artificial recognitionresult of each valuable corpus, are beneficial to reduce the workload ofthe artificial corpus tagging, and the optimization efficiency of intentrecognition optimization can be effectively improved withoutcompromising the optimization effect.

Further, the first recognition result of each original corpus furtherincludes an intent confidence of each intent in the first intent setcorresponding to the original corpus; and the determining, according tothe first recognition result of each original corpus, a tagging value ofeach original corpus, includes:

determining, according to an intent confidence of each intent in thefirst intent set corresponding to the original corpus, a confidenceinformation entropy, a highest intent confidence and a secondary highestintent confidence corresponding to each of the original corpus; and

determining, according to the confidence information entropy, thehighest intent confidence and the secondary highest intent confidencecorresponding to each of the original corpus, a tagging value of each ofthe original corpus.

By means of determining the valuable corpus according to the intentconfidence of each intent in the first intent set corresponding to theoriginal corpus, the determined valuable corpus can represent all theoriginal corpus, and thus the optimization effect is good and theoptimization efficiency is high by using the valuable corpus to optimizethe intent set.

Further, the acquiring the second recognition result of each originalcorpus includes:

determining that the second recognition result of an original corpus isthe same tagging result as the first recognition result, if the secondrecognition result of the original corpus is not acquired.

When a modeling recognition result of an original corpus is approved bythe artificial tagger, the original corpus can be skipped withouttagging and the artificial recognition result of the original corpus isdetermined to be the same as the model recognition result, which isbeneficial reduce the workload of artificial taggers and improve theefficiency of the intent set optimization.

Further, the method further includes: selecting an intent from thesecond intent set to perform corpus tagging using the intent recognitionmodel after the second intent set is obtained.

Performing corpus intent recognition using the second intent set duringthe daily work of the intent recognition model after the optimizedsecond intent set is obtained, is beneficial to improve the accuracy andrecognition efficiency of corpus intent recognition.

Further, the method further includes: training the intent recognitionmodel according to the second recognition result of each originalcorpus.

Retraining the intent recognition model to achieve the optimization workof the intent recognition model by using artificial recognition resultof each original corpus, i.e. according to objective intent of eachoriginal corpus, is beneficial to improve the recognition precision andrecognition efficiency of the corpus intent recognition and to improveservice effect of the automatic question and answering service.

In a second aspect of the present application, an intent recognitionoptimization processing apparatus is provided, including: at least oneprocessor; and a memory communicatively connected with the at least oneprocessor, wherein: the memory stores thereon instructions executable bythe at least one processor, and the instructions are executed by the atleast one processor to cause the at least one processor to implementfollowing steps:

acquiring a first intent set and at least one original corpus;

acquiring a first recognition result of each original corpus, where thefirst recognition result of any one of the original corpus includes afirst intent corresponding to the original corpus recognized by anintent recognition model;

acquiring a second recognition result of each original corpus, whereinthe second recognition result of any one of the original corpus includesa second intent corresponding to the original corpus obtained throughartificial recognition; and;

performing optimization processing on the first intent set, according tothe first recognition result and the second recognition result of eachoriginal corpus, to obtain a second intent set.

Further, the instructions are executed by the at least one processor tocause the at least one processor to implement following steps:

determining a first corpus number and a second corpus numbercorresponding to each intent in the first intent set according to thefirst recognition result and the second recognition result of eachoriginal corpus; and

performing optimization processing on the first intent set to obtain asecond intent set according to the first corpus number and the secondcorpus number corresponding to each intent in the first intent set; and

where the determining a first corpus number and a second corpus numbercorresponding to each intent in the first intent set, includes:

determining, according to the first recognition result of each of theoriginal corpus, the number of original corpus that the first intentthereof is the one of the first intent set as the first corpus number;and

determining, according to the second recognition result of each of theoriginal corpus that the first intent thereof is the one of the firstintent set, the number of original corpus that the second intent thereofis the one of the first intent set as the second corpus number.

Further, the instructions are executed by the at least one processor tocause the at least one processor to implement following steps:

determining a first accuracy rate of the first intent set, according tothe first corpus number and the second corpus number corresponding toeach intent in the first intent set;

repeating following steps, until whether every two intents in the firstintent set need to be merged is determined: determining, according tothe first corpus number and the second corpus number corresponding toany two intents in the first intent set, a second accuracy ratecorresponding to a third intent set obtained by merging the two intents,and determining that the two intents need to be merged when the secondaccuracy rate is higher than the first accuracy rate; and

merging any two intents that need to be merged in the first intent set,to obtain the second intent set.

Further, the first accuracy rate is the ratio of a sum of the secondcorpus number corresponding to all intents in the first intent set to asum of the first corpus number corresponding to all intents in the firstintent set; and the second accuracy rate is the ratio of a sum of thesecond corpus number corresponding to all intents in the third intentset to a sum of the first corpus number corresponding to all intents inthe third intent set.

Further, the instructions are executed by the at least one processor tocause the at least one processor to implement following steps:

determining a tagging value of each original corpus according to thefirst recognition result of each original corpus, and determine anoriginal corpus that the tagging value thereof exceeds a set thresholdas a valuable corpus, to obtain at least one valuable corpus; and

acquiring the second recognition result of each valuable corpus.

Further, the first recognition result of each original corpus furtherincludes an intent confidence of each intent in the first intent setcorresponding to the original corpus; and the instructions are executedby the at least one processor to cause the at least one processor toimplement following steps:

determining a confidence information entropy, a highest intentconfidence and a secondary highest intent confidence corresponding toeach original corpus according to the intent confidence of each intentin the first intent set corresponding to the original corpus; and

determining a tagging value of each original corpus according to theconfidence information entropy, the highest intent confidence and thesecondary highest intent confidence corresponding to each originalcorpus.

Further, the instructions are executed by the at least one processor tocause the at least one processor to implement following steps:

determining that the second recognition result of an original corpus isthe same tagging result as the first recognition result, if the secondrecognition result of the original corpus is not acquired.

Further, the instructions are executed by the at least one processor tocause the at least one processor to implement following steps:

training the intent recognition model according to the secondrecognition result of each original corpus.

Further, the instructions are executed by the at least one processor tocause the at least one processor to implement following steps:

selecting an intent from the second intent set to perform corpus taggingusing the intent recognition model after the second intent set isobtained.

In a third aspect of the present application, a non-transitory computerreadable storage medium storing computer instructions is provided, andthe computer instructions are used to implement the method provided inany implementation of the first aspect.

One of the embodiments of aforementioned application has the followingadvantages or benefits: a reasonable classification granularity of theintent in the intent set, high intent recognition efficiency, and a goodeffect of the intent recognition. By adopting the technical meansincluding: acquiring a first intent set and at least one originalcorpus; acquiring a first recognition result of each original corpus,where the first recognition result of any one of the original corpusincludes a first intent corresponding to the original corpus recognizedby an intent recognition model; acquiring a second recognition result ofeach original corpus, where the second recognition result of any one ofthe original corpus includes a second intent corresponding to theoriginal corpus obtained through artificial recognition; and performingoptimization processing on the first intent set to obtain a secondintent set according to the first recognition result and the secondrecognition result of each original corpus, the problem of low intentrecognition efficiency and unguaranteed accuracy due to the too fine ortoo vague intent classification granularity of the intent set in theintent recognition related technology is overcome.

Other effects of the aforementioned possible implementations will bedescribed below in conjunction with specific embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are for a better understanding of the presentsolution and are not intended to limit the application.

FIG. 1 is a schematic flowchart of an intent recognition optimizationprocessing method provided by an embodiment of the present application;

FIG. 2 is a schematic flowchart of another intent recognitionoptimization processing method provided by an embodiment of the presentapplication;

FIG. 2a is a schematic diagram of an intent merging according to anembodiment of the present application;

FIG. 3 is a schematic structural diagram of an intent recognitionoptimization processing apparatus according to an embodiment of thepresent application;

FIG. 4 is a schematic structural diagram of another intent recognitionoptimization processing apparatus according to an embodiment of thepresent application;

FIG. 5 is a schematic structural diagram of an intent recognitionoptimization processing apparatus according to an embodiment of thepresent application.

DESCRIPTION OF EMBODIMENTS

The following describes exemplary embodiments of the present applicationwith reference to the accompanying drawings, which include variousdetails of the embodiments of the present application to facilitateunderstanding, and should be regarded as merely exemplary. Therefore,those of ordinary skilled in the art should realize that various changesand modifications can be made to the embodiments described hereinwithout departing from the scope and spirit of the present application.Likewise, for clarity and conciseness, descriptions of well-knownfunctions and structures are omitted in the following description.

Application scenarios of this application: with rapid development ofinternet technologies, the automatic question and answering service hasbeen widely promoted and applied, for example, in common areas such ase-commerce customer service, information query, self-service orderprocessing, and search using engine search, automatic question andanswer services are widely used. During the automatic question andanswer service, the server obtains a corpus entered by a user,recognizes an intent and slot of the obtained corpus, and then returnsthe answer to the question matching the corpus or provides correspondingservices, where intent recognition of the acquired corpus is one of theimportant contents thereof. In the process of intent recognition of thecorpus, it is necessary to determine and provide an intent set inadvance, so that an intent recognition model or an artificial tagger canselect intents from the intent set for corpus tagging. In the prior art,after obtaining the intent set by learning the sample data, the fixedintent set obtained by learning is used for selecting the intent thereinby the intent recognition model or the artificial tagger to perform thecorpus tagging.

However, when the classification granularity of the intent set obtainedby learning is too fine or too vague, the recognition efficiency and theaccuracy rate of the corpus intent recognition would be significantlyaffected, which in turn affects the rate and the accuracy of the corpustagging.

The intent recognition optimization processing method, the apparatus,the device, and the storage medium provided in this application areintended to solve the above technical problems.

FIG. 1 is an intent recognition optimization processing method providedby an embodiment of the present application. As shown in the FIG. 1, themethod includes:

Step 101, acquiring a first intent set and at least one original corpus.

In this embodiment, specifically, the execution entity of thisembodiment is a terminal device or a server or a controller provided onthe terminal device, or other apparatuses or devices that can executethis embodiment. In this embodiment, the execution entity is theapplication software set on the terminal device as an example fordescription.

The first intent set obtained is the original intent set beforeoptimization, including at least one category of intent. The function ofthe first intent set is for intent recognition model or artificialtagger to select intents for corpus tagging. The first intent set usedin different application scenarios for corpus intent recognition isdifferent. Exemplarily, in the e-commerce customer service, the firstset of intents used in corpus intent recognition includes intents ofpurchase, product information consultation, product orientationselection, preferential consultation, etc. In fitness self-service, thefirst intent set used in corpus intent recognition includes intents ofbasic information, consulting coach, consulting address, generalgreeting, consulting registration, etc., and the first intent set usedin different application scenarios is different.

The at least one original corpus acquired may be an untagged originalcorpus, or a corpus that has been tagged using an intent recognitionmodel, in which case the first recognition result that has been taggedusing the intent recognition model is included. Specifically, the corpustagged with the intent recognition model in the working process of theautomatic question-answering service can be selected, and the firstintent set and the intent recognition model can be optimized byobtaining historical data in the working process of the automaticquestion answering service.

Step 102, acquiring a first recognition result of each original corpus,where the first recognition result of any one of the original corpusincludes a first intent corresponding to the original corpus recognizedby an intent recognition model.

In this embodiment, specifically, when the acquired original corpus isan untagged corpus, the intent recognition model is used to performintent recognition and tagging each unlabeled original corpus to obtainthe first recognition result tagged with the intent recognition model.When the acquired original corpus is a tagged corpus, the firstrecognition result of each tagged original corpus is directly obtained.The first recognition result of any one of the original corpus includesthe first intent corresponding to the original corpus recognized by theintent recognition model, that is, the first recognition result is themodel recognition result, and the first intent is the predictive intentof the original corpus. The first recognition result of any one of theoriginal corpus includes not only the first intent corresponding to theoriginal corpus recognized by the intent recognition model, but also theintent confidence of the original corpus corresponding to each intent inthe first intent set. The method for determining the intent confidencecan be implemented by using the existing technology, which will not berepeated in this application.

Step 103, acquiring a second recognition result of each original corpus,where the second recognition result of any one of the original corpusincludes a second intent corresponding to the original corpus obtainedthrough artificial recognition.

In this embodiment, specifically, in the optimization processing of thefirst intent set or the intent recognition model, the objective intentcorresponding to each original corpus needs to be used, where theobjective intent corresponding to each original corpus is an intentrecognized artificially. The second recognition result of each originalcorpus is acquired through intent recognition and corpus tagging eachoriginal corpus by artificial tagger. The second recognition result ofany one of the original corpus includes the second intent correspondingto the original corpus obtained through artificial recognition, that is,the second recognition result is an artificial recognition result, andthe second intent is an objective intent.

When acquiring the second recognition result of each original corpus,the first intent set is displayed to the artificial tagger for theartificial tagger to select the intent in the first intent set forcorpus tagging, which can effectively reduce the professionalrequirements of corpus tagging and at the same time help improve theefficiency of corpus tagging. When the acquired original corpus is analready tagged corpus, the first recognition result of each originalcorpus is displayed to the artificial tagger for the artificial taggerto judge the tagging result. When the artificial tagger agrees to thefirst recognition result of a certain original corpus, the originalcorpus can be skipped directly, and at this time, it is assumed that thesecond recognition result of the original corpus is the same as thefirst recognition result, which can effectively improve the efficiencyof artificial tagging of the original corpus, thereby effectivelyimproving the optimization efficiency of the first intent set, andconducive to improving the optimization efficiency of the intentrecognition model.

Step 104, performing optimization processing on the first intent set toobtain a second intent set according to the first recognition result andthe second recognition result of each original corpus.

In this embodiment, specifically, optimizing the first intent set,according to the first recognition result and the second recognitionresult of each original corpus, that is, according to the modelrecognition result and artificial recognition result of each originalcorpus. Since the second recognition result is the second intentcorresponding to the artificially recognized original corpus, that is,the objective intent of the original corpus, according to the objectiveintent and predictive intent of each original corpus, the correctrecognition result and the deviation recognition result corresponding toeach of the intent in the first intent set can be determined. Thecorrect recognition result is the number of original corpus of which theobjective intent and the predictive intent are the same, and thedeviation recognition result is the number of original corpus of whichthe objective intent is different from the predictive intent. Thuswhether the classification of intent in the first intent set isreasonable can be determined, and the second intent set is obtained byoptimizing the first intent set.

After obtaining the second intent set, performing corpus tagging toother corpora to realize automatic question-answer services by using theintent recognition model to select intents in the second intent set;also could be performing other optimization work by corpus tagging anintent selected in the second intent set for corpus tagging byartificial tagger. Since the second intent set is an optimized intentset, using the second intent set to perform intent recognitionprocessing of the corpus realizes the optimization of corpus intentrecognition.

In this embodiment, the second intent set is obtained by means of:acquiring a first intent set and at least one original corpus; acquiringa first recognition result of each original corpus, where the firstrecognition result of any one of the original corpus includes a firstintent corresponding to the original corpus recognized by an intentrecognition model; acquiring a second recognition result of eachoriginal corpus, where the second recognition result of any one of theoriginal corpus includes a second intent corresponding to the originalcorpus obtained through artificial recognition; and performingoptimization processing on the first intent set according to the firstrecognition result and the second recognition result of each originalcorpus. The optimization of the first intent set is performed accordingto the first recognition result and the second recognition result ofeach original corpus, i.e., according to the model recognition resultand manual recognition result of each original corpus, where the methodof optimizing an intent set is simple, and the optimization referencefactors are diversified, and thus it is beneficial to realize effectiveintent set optimization, and then, it is beneficial to improve therecognition accuracy and efficiency of intent recognition by optimizingthe intent set.

FIG. 2 is a schematic flowchart of another intent recognitionoptimization processing method provided by an embodiment of the presentapplication. As shown in the FIG. 2, the method includes:

Step 201, acquiring a first intent set and at least one original corpus.

In this embodiment, specifically, for this step, reference may be madeto step 101 in FIG. 1, and details are not described herein again.

Step 202, acquiring a first recognition result and a second recognitionresult of each original corpus, where the first recognition result ofany one of the original corpus includes a first intent corresponding tothe original corpus recognized by the intent recognition model and thesecond recognition result includes a second intent corresponding to theoriginal corpus obtained through artificial recognition.

In this embodiment, specifically, before acquiring the secondrecognition result of each original corpus, in order to improve theoptimization efficiency of intent recognition optimization and reducethe workload of artificial tagger, determine a tagging value of eachoriginal corpus according to the first recognition result of eachoriginal corpus, and obtain the valuable corpus by filtering theacquired original corpus according to the tagging value of each originalcorpus, display the obtained valuable corpus to the artificial tagger,and obtain the second recognition result of the valuable corpus of theartificial tagger.

Determining the confidence information entropy, the highest intentconfidence and the second highest intent confidence of the originalcorpus according to the intent confidence of each of the intent in thefirst intent set corresponding to any one of the original corpus. Agreater confidence information entropy represents that the intentconfidence of each intent in the first intent set corresponding to theoriginal corpus is more average, which indicates that the uncertainty ofthe predictive intent of the original corpus is higher, and thus thetagging value of the original corpus is higher. A greater highest intentconfidence represents that the original corpus is more concentrated to acertain intent, and thus the uncertainty of the predictive intent islower, and the tagging value is lower; a larger confidence differencebetween the highest intent confidence and the second highest intentconfidence represents that the original corpus is more concentrated to acertain intent, and thus the uncertainty of the predictive intent islower, and the tagging value is lower.

Using characters s, m, d to respectively represent the confidenceinformation entropy, the highest intent confidence, and the differencebetween the highest intent confidence and the second highest intentconfidence of any original corpus, and normalize s, m, d respectively,and then the tagging value of the original corpus is determined asv=(1−s+m+d)/3. Take the original corpus that the tagging value thereofexceeds a set threshold as the valuable corpus, obtain the secondrecognition result of the valuable corpus and perform optimizationprocessing on the first intent set according to the first recognitionresult and the second recognition result of the valuable corpus,according to which, the workload of the artificial tagger can beeffectively reduced without affecting the optimization effect of theintent set, and the optimization efficiency of the intent set can beimproved. The second recognition result of the valuable corpus can alsobe used to retrain the intent recognition model to optimize the intentrecognition model, which has a high efficiency and a significantoptimization effect.

After determining the tagging value of each original corpus, classifyand mark the original corpus according to preset division thresholds,and send the original corpus with the classification mark to theartificial tagger for corpus tagging according to the classificationmark, where the artificial tagger can determine whether to pay extraattention or quickly browse for tagging according to the classificationmark. When the artificial tagger is faced with a large amount oforiginal corpuses, the classification mark is conducive to helping theartificial tagger to quickly learn the value of each original corpus,and thus can help the artificial tagger adjust the tagging speed andunit tagging time according to the tagging value, which can effectivelyimprove the efficiency of obtaining the second recognition result of theoriginal corpus. Exemplarily, use a red mark to identify the originalcorpus that the tagging value thereof is higher than the first setthreshold; use a green mark to identify the original corpus that thetagging value thereof is lower than the second set threshold; and use ayellow mark to identify the original corpus that the tagging valuethereof is higher than the first set threshold and lower than the secondset threshold. It can be used for artificial tagger to intuitively andquickly learn the value of each original corpus based on the mark incolor of the original corpus. When the second recognition result of theoriginal corpus is obtained, the artificial tagger can be prompted tofocus on tagging the original corpuses with mark in red or mark inyellow.

Step 203, determining, according to the first recognition result and thesecond recognition result of each original corpus, a first corpus numberand a second corpus number corresponding to each intent in the firstintent set.

In this embodiment, specifically, when using the intent recognitionmodel to recognize the original corpus, the intent recognition modelselects one of the intents from the first intent set as the first intent(that is, the predictive intent) of the original corpus, and when usingthe artificial tagger to recognize the original corpus, the artificialtagger also selects one of the intents from the first intent set as thesecond intent (that is, the objective intent) of the original corpus.Therefore, for the acquired at least one original corpus, the set offirst intent of the at least one original corpus is a subset of thefirst intent set, and the set of second intent set is also a subset ofthe first intent set.

Determining the first corpus number and the second corpus numbercorresponding to any intent in the first intent set, includes:determining, according to the first recognition result of each of theoriginal corpus, the number of the original corpus that the first intentthereof is the one of the original corpus as the first corpus number;determining, according to the second recognition result of each of theoriginal corpus that the first intent thereof is the one of the firstintent set, the number of original corpus that the second intent thereofis the one of the first intent set as the second corpus number. For anyoriginal corpus, when the model recognition result of the originalcorpus is the same as the artificial recognition result, that is, whenthe predictive intent of the original corpus is the same as theobjective intent, it is determined that the model recognition result ofthe original corpus is correct. Determine the first corpus number of anyintent in the first intent set, where the first corpus number is thenumber of original corpus that the first intent thereof is the one inthe said intent, determine, according to the second recognition resultof each of the original corpus that the first intent thereof is the saidintent, the number of original corpus that the second intent thereof isthe said intent as the second corpus number. When the ratio of thesecond corpus number to the first corpus number corresponding to anintent is higher, the accuracy of intent recognition for the intent ishigher and the intent recognition effect corresponding to the intent isbetter.

Step 204, performing optimization processing on the first intent set,according to the first corpus number and the second corpus numbercorresponding to each intent in the first intent set, to obtain a secondintent set.

In this embodiment, specifically, the first intent set includes at leastone intent, and the granularity of intent classification, which is toofine or too vague, will affect the recognition efficiency andrecognition accuracy of the intent recognition. Therefore, it isnecessary to determine whether to optimize the intent set and how tooptimize the intent set according to the accuracy of the intent set.

Determining a first accuracy rate of the first intent set, according tothe first corpus number and the second corpus number corresponding toeach intent in the first intent set; repeating following steps, untilwhether every two intents in the first intent set need to be merged isdetermined: determining, according to the first corpus number and thesecond corpus number corresponding to any two intents in the firstintent set, a second accuracy rate corresponding to a third intent setobtained by merging the two intents, and determining that the twointents need to be merged when the second accuracy rate is higher thanthe first accuracy rate; merging any two intents that need to be mergedin the first intent set, to obtain the second intent set. The firstaccuracy rate is a ratio of a sum of the second corpus numbercorresponding to all intents in the first intent set to a sum of thefirst corpus number corresponding to all intents in the first intentset; and the second accuracy rate is the ratio of a sum of the secondcorpus number corresponding to all intents in the third intent set to asum of the first corpus number corresponding to all intents in the thirdintent set.

FIG. 2a is a schematic diagram of an intent merging according to anembodiment of the present application. The first corpus numbercorresponding to intent A, intent B, and intent C in the first intentset are 6, 7, and 7, respectively. The second corpus numbercorresponding to intent A, intent B, and intent C in the first intentset are 1, 3, and 4, respectively. The first accuracy rate of the firstintent set is 0.4. Assuming that intent A and intent B are merged toobtain intent D. The first corpus number of the merged third intent setis 13, 7, respectively. The second corpus number is 10, 4, respectively,and the second accuracy rate of the third intent set is 0.7. The secondaccuracy rate is higher than the first accuracy rate, indicating thatthe design of the intent classification in the third intent set is morereasonable, and the intent classification of the first intent set needsto be merged. This example is an illustrative description of the firstintent set. When the number of intents in the first intent set is toomuch and the granularity of intent classification is too fine, forexample, in the automatic question-answering service about hotel orders,there are as many as dozens of intents in a common intent set, using theintent recognition model to select intents in the first intent set withunreasonable classification and very fine classification for corpusintent recognition has the disadvantages that the efficiency of intentrecognition is low and the recognition accuracy is affected. Use themethod of this embodiment to simplify the number of intents and theclassification of intents in a concentration of intents on the basis ofensuring the accuracy of intent recognition, which can effectivelyimprove the recognition efficiency of intent recognition and isbeneficial to realize more efficient and intelligent automated services.

After completing the optimization processing for the first intent set,use the optimized second intent set to participate in the intentrecognition of the corpus which is specifically used for the intentrecognition model or manual annotators to select intents from the secondintent set for intent recognition and corpus annotation.

In a possible implementation, use the second recognition result of theoriginal corpus to retrain the intent recognition model to optimize theintent recognition model, that is, use the objective intent of theoriginal corpus to train the intent recognition model to obtain anintent recognition model with better performance and higher recognitionaccuracy. The method of using the second recognition result of theoriginal corpus to train the intent recognition model can be implementedby using existing technology, which will not be repeated in thisapplication.

In this embodiment, acquire a first intent set and at least one originalcorpus; acquire a first recognition result and a second recognitionresult of each original corpus, where the first recognition result ofany one of the original corpus includes a first intent corresponding tothe original corpus recognized by the intent recognition model and thesecond recognition result includes a second intent corresponding to theoriginal corpus obtained through artificial recognition; determine,according to the first recognition result and the second recognitionresult of each original corpus, a first corpus number and a secondcorpus number corresponding to each intent in the first intent set; andperform optimization processing on the first intent set, according tothe first corpus number and the second corpus number corresponding toeach intent in the first intent set, to obtain a second intent set.According to the first corpus number and the second corpus numbercorresponding to each of the intent in the first intent set, merge anytwo intents in the first intent set, and judge whether the accuracy ofthe intent set before and after the merge is improved. When the accuracyrate increases, it means that the two intents are poorlydistinguishable, and their corresponding original corpus is lessdifferent, and thus the answers to questions or services providedaccording to the two intents can't specifically address different needsof users. There is a problem that the intent classification is too fineor too vague for the two intents, so it is necessary to merge the twointents. Obviously, after determining whether any two intents in thefirst intent set need to be merged, and after merging the intents thatneed to be merged, an intent set with optimized intent classificationcan be obtained. The number of intents in the optimized intent set canbe effectively simplified, which is conducive to improving therecognition efficiency and recognition accuracy of intent recognition,and is conducive to improving the service effect of the automaticquestion-answering service. The intent set optimization method issimple. When automatic question-answering service products is providedto service providers, service providers can optimize the intent set andintent recognition model by using artificial corpus tagging. The methodfor optimizing automatic question-answering service product is simpleand the optimization efficiency is high.

FIG. 3 is a schematic structural diagram of an intent recognitionoptimization processing apparatus according to an embodiment of thepresent application. As shown in the FIG. 3, the apparatus includes:

a first acquisition unit 1, configured to acquire a first intent set andat least one original corpus;

a second acquisition unit 2, configured to acquire a first recognitionresult of each original corpus, where the first recognition result ofany one of the original corpus includes a first intent corresponding tothe original corpus recognized by the intent recognition model;

a third acquisition unit 3, configured to acquire a second recognitionresult of each original corpus, where the second recognition result ofany one of the original corpus includes a second intent corresponding tothe original corpus obtained through artificial recognition;

a first processing unit 4, configured to perform optimization processingon the first intent set, according to the first recognition result andthe second recognition result of each original corpus, to obtain asecond intent set.

In this embodiment, the second intent set is obtained by means of:acquiring a first intent set and at least one original corpus; acquiringa first recognition result of each original corpus, where the firstrecognition result of any one of the original corpus includes a firstintent corresponding to the original corpus recognized by an intentrecognition model; acquiring a second recognition result of eachoriginal corpus, where the second recognition result of any one of theoriginal corpus includes a second intent corresponding to the originalcorpus obtained through artificial recognition; and performingoptimization processing on the first intent set according to the firstrecognition result and the second recognition result of each originalcorpus. The optimization of the first intent set is performed accordingto the first recognition result and the second recognition result ofeach original corpus, i.e., according to the model recognition resultand manual recognition result of each original corpus, where the methodof optimizing an intent set is simple, and the optimization referencefactors are diversified, and thus it is beneficial to realize effectiveintent set optimization, and then, it is beneficial to improve therecognition accuracy and efficiency of intent recognition by optimizingthe intent set.

FIG. 4 is a schematic structural diagram of another intent recognitionoptimization processing apparatus according to an embodiment of thepresent application. On the basis of FIG. 3, as shown in FIG. 4,

the first processing unit 4, including:

a first processing subunit 41, configured to determine a first corpusnumber and a second corpus number corresponding to each intent in thefirst intent set according to the first recognition result and thesecond recognition result of each original corpus; and

a second processing subunit 42, configured to perform optimizationprocessing on the first intent set to obtain a second intent setaccording to the first corpus number and the second corpus numbercorresponding to each intent in the first intent set; and

the determining the first corpus number and the second corpus numbercorresponding to any intent in the first intent set, includes:

determining, according to the first recognition result of each of theoriginal corpus, the number of the original corpus that the first intentthereof is the one of the first intent set as the first corpus number;and

determining, according to the second recognition result of each of theoriginal corpus that the first intent thereof is the one of the firstintent set, the number of original corpus that the second intent thereofis the one of the first intent set as the second corpus number.

The second processing subunit 42 includes:

a first processing module 421, configured to determine a first accuracyrate of the first intent set, according to the first corpus number andthe second corpus number corresponding to each intent in the firstintent set;

a second processing module 422, configured to repeat following steps,until whether every two intents in the first intent set need to bemerged is determined: determining, according to the first corpus numberand the second corpus number corresponding to any two intents in thefirst intent set, a second accuracy rate corresponding to a third intentset obtained by merging the two intents, and determining that the twointents need to be merged when the second accuracy rate is higher thanthe first accuracy rate; and

a third processing module 423, configured to merge any two intents thatneed to be merged in the first intent set, to obtain the second intentset.

The first accuracy rate is a ratio of a sum of the second corpus numbercorresponding to all intents in the first intent set to a sum of thefirst corpus number corresponding to all intents in the first intentset; and the second accuracy rate is the ratio of a sum of the secondcorpus number corresponding to all intents in the third intent set to asum of the first corpus number corresponding to all intents in the thirdintent set.

The third acquisition unit 3 includes:

a third processing subunit 31, configured to determine a tagging valueof each original corpus according to the first recognition result ofeach original corpus, and determine an original corpus that the taggingvalue thereof exceeds a set threshold as a valuable corpus, to obtain atleast one valuable corpus; and

a first acquisition subunit 32, configured to acquire the secondrecognition result of each valuable corpus.

The first recognition result of each original corpus further includes anintent confidence of each intent in the first intent set correspondingto the original corpus; and the third processing subunit 31 includes:

a fourth processing module 311, configured to determine a confidenceinformation entropy, a highest intent confidence and a secondary highestintent confidence corresponding to each original corpus according to theintent confidence of each intent in the first intent set correspondingto the original corpus; and

a fifth processing module 312, configured to determine a tagging valueof each original corpus according to the confidence information entropy,the highest intent confidence and the secondary highest intentconfidence corresponding to each original corpus.

The third acquisition unit 3 further includes:

a fourth processing subunit 33, configured to determine that the secondrecognition result of an original corpus is the same tagging result asthe first recognition result, if the second recognition result of theoriginal corpus is not acquired.

The apparatus further includes:

a second processing module 5, configured to train the intent recognitionmodel according to the second recognition result of each originalcorpus.

the apparatus further includes:

a third processing module 6, configured to select an intent from thesecond intent set optimized by intent recognition to perform corpustagging using the intent recognition model after the second intent setis obtained.

In this embodiment, acquire a first intent set and at least one originalcorpus; acquire a first recognition result and a second recognitionresult of each original corpus, where the first recognition result ofany one of the original corpus includes a first intent corresponding tothe original corpus recognized by the intent recognition model and thesecond recognition result includes a second intent corresponding to theoriginal corpus obtained through artificial recognition; determine,according to the first recognition result and the second recognitionresult of each original corpus, a first corpus number and a secondcorpus number corresponding to each intent in the first intent set; andperform optimization processing on the first intent set, according tothe first corpus number and the second corpus number corresponding toeach intent in the first intent set, to obtain a second intent set.According to the first corpus number and the second corpus numbercorresponding to each of the intent in the first intent set, merge anytwo intents in the first intent set, and judge whether the accuracy ofthe intent set before and after the merge is improved. When the accuracyrate increases, it means that the two intents are poorlydistinguishable, and their corresponding original corpus is lessdifferent, and thus the answers to questions or services providedaccording to the two intents can't specifically address different needsof users. There is a problem that the intent classification is too fineor too vague for the two intents, so it is necessary to merge the twointents. Obviously, after determining whether any two intents in thefirst intent set need to be merged, and after merging the intents thatneed to be merged, an intent set with optimized intent classificationcan be obtained. The number of intents in the optimized intent set canbe effectively simplified, which is conducive to improving therecognition efficiency and recognition accuracy of intent recognition,and is conducive to improving the service effect of the automaticquestion-answering service. The intent set optimization method issimple. When automatic question-answering service products is providedto service providers, service providers can optimize the intent set andintent recognition model by using artificial corpus tagging. The methodfor optimizing automatic question-answering service product is simpleand the optimization efficiency is high.

According to the embodiments of the present application, the presentapplication also provides an electronic device and a readable storagemedium.

As shown in FIG. 5, it is a schematic structural diagram of an intentrecognition optimization processing device according to intentrecognition optimization an embodiment of the present application.Electronic devices are intended to represent various forms of digitalcomputers, such as laptop computers, desktop computers, workstations,personal digital assistants, servers, blade servers, mainframecomputers, and other suitable computers. Electronic devices can alsorepresent various forms of mobile devices, such as personal digitalprocessing, cellular phones, smart phones, wearable devices, and othersimilar computing devices. The components shown herein, theirconnections and relationships, and their functions are merely examples,and are not intended to limit the implementation of the applicationdescribed and/or required herein.

As shown in FIG. 5, the electronic device includes: one or moreprocessors 501, memory 502, and interfaces for connecting variouscomponents, including a high-speed interface and a low-speed interface.The various components are connected to each other by using differentbuses, and can be installed on a common motherboard or installed inother ways as required. The processor may process instructions executedin the electronic device, including instructions stored in or on thememory to display graphical information of the GUI on an externalinput/output device (such as a display device coupled to an interface).In other embodiments, multiple processors and/or multiple buses may beused with multiple memories and multiple memories if necessary.Similarly, multiple electronic devices can be connected, and each deviceprovides some necessary operations (for example, as a server array, agroup of blade servers, or a multi-processor system). In FIG. 5, aprocessor 501 is taken as an example.

The memory 502 is a non-transitory computer-readable storage mediumprovided by this application. The memory stores instructions that can beexecuted by at least one processor, so that the at least one processorexecutes the intent recognition optimization processing method providedin this application. The non-transitory computer-readable storage mediumof the present application stores computer instructions, and thecomputer instructions are used to make a computer execute the intentrecognition optimization processing method provided by the presentapplication.

As a non-transitory computer-readable storage medium, the memory 502 canbe used to store non-transitory software programs, non-transitorycomputer executable programs, and modules, such as the programinstructions/modules corresponding to the intent recognitionoptimization processing method in the embodiment of the application.(For example, the acquisition unit 1, the first processing unit 2 andthe second processing unit 3 shown in FIG. 3) The processor 501 executesvarious functional applications and data processing of the server byrunning non-transitory software programs, instructions, and modulesstored in the memory 502, that is, implementing the intent recognitionoptimization processing method in the foregoing method embodiment.

The memory 502 may include an area for storing program and an area forstoring data, where the area for storing program can store an operatingsystem and an application program required by at least one function; thestorage data area may store data and the like created according to theuse of electronic device for intent recognition optimization processing.In addition, the memory 502 may include a high-speed random accessmemory, and may also include a non-transitory memory, such as at leastone magnetic disk storage component, a flash memory component, or othernon-transitory solid-state storage components. In some embodiments, thememory 502 may optionally include a memory remotely provided withrespect to the processor 501, and these remote memories may be connectedto an electronic device for intent recognition optimization processingvia a network. Examples of the aforementioned networks include, but arenot limited to, the internet, corporate intranets, local area networks,mobile communication networks, and combinations thereof.

The electronic device of the intent recognition optimization processingmethod may further include: input apparatus 503 and output apparatus504. The processor 501, the memory 502, the input apparatus 503, and theoutput apparatus 504 may be connected by a bus or other means. The busconnection is taken as an example in FIG. 5.

The input apparatus 503 can receive inputted number or characterinformation, and generate key signal input related to user settings andfunction control of the electronic device for intent recognitionoptimization processing. For example, touch screen, keypad, mouse, trackpad, touch pad, pointing stick, one or more mouse buttons, track ball,joystick and other input apparatus. The output device 504 may include adisplay device, an auxiliary lighting apparatus (for example, LED), atactile feedback apparatus (for example, a vibration motor), and thelike. The display device may include, but is not limited to, a liquidcrystal display (LCD), a light emitting diode (LED) display, and aplasma display. In some embodiments, the display device may be a touchscreen.

Various implementations of the systems and technologies described hereincan be implemented in digital electronic circuit systems, integratedcircuit systems, application specific ASICs (application specificintegrated circuits), computer hardware, firmware, software, and/orcombinations thereof. These various implementations may include:implemented in one or more computer programs, the one or more computerprograms may be executed and/or interpreted on a programmable systemincluding at least one programmable processor, and the programmableprocessor may be a dedicated or general programmable processor, whichcan receive data and instructions from the storage system, at least oneinput apparatus, and at least one output apparatus, and transmit thedata and instructions to the storage system, the at least one inputapparatus, and the at least one output apparatus.

These computing programs (also called programs, software, softwareapplications, or code) include machine instructions for programmableprocessors, and can use high-level process and/or object-orientedprogramming language, and/or assembly/machine language to implementthese calculation programs. As used herein, the terms “machine-readablemedium” and “computer-readable medium” refer to any computer programproduct, device, and/or apparatus used to provide machine instructionsand/or data to a programmable processor (For example, magnetic disks,optical disks, memory, programmable logic devices (PLD)), includingmachine-readable media that receive machine instructions asmachine-readable signals. The term “machine-readable signal” refers toany signal used to provide machine instructions and/or data to aprogrammable processor.

In order to provide interaction with users, the systems and techniquesdescribed here can be implemented on a computer that has: displayapparatuses used to display information to users (for example, CRT(Cathode Ray Tube) or LCD (Liquid Crystal Display) monitors); as well asa keyboard and a pointing apparatus (for example, a mouse or atrackball), the user can provide input to the computer through thekeyboard and the pointing apparatus. Other types of apparatuses can alsobe used to provide interaction with users; for example, the feedbackprovided to the user can be any form of sensory feedback (for example,visual feedback, auditory feedback, or tactile feedback); and can useany form (including sound input, voice input or tactile input) toreceive input from the user.

The systems and technologies described herein can be implemented in acomputing system that includes back-end components (for example, as adata server), or a computing system that includes middleware components(for example, an application server), or a computing system thatincludes front-end components (for example, a user computer with agraphical user interface or web browser, through which the user caninteract with the implementation of the system and technology describedherein), or any combination of back-end components, middlewarecomponents, or front-end components in a computing system. Thecomponents of the system can be connected to each other through any formor medium of digital data communication (for example, a communicationnetwork). Examples of communication networks include: local area network(LAN), wide area network (WAN), and the Internet.

The computer system can include clients and servers. The client andserver are generally far away from each other and usually interactthrough a communication network. The relationship between the client andthe server is generated through computer programs running on thecorresponding computers and having a client-server relationship witheach other.

For the principles and beneficial effects of the intent recognitionoptimization processing system provided in this embodiment, refer to theprinciples and beneficial effects of the intent recognition optimizationprocessing method in FIG. 1 to FIG. 2, and details are not repeatedhere.

For the principles and beneficial effects of the intent recognitionoptimization processing method provided in this embodiment, refer to theprinciples and beneficial effects of the intent recognition optimizationprocessing method in FIG. 1 to FIG. 2, and details are not repeatedhere.

In the embodiments of the present application, the above-mentionedembodiments can refer to each other and learn from each other, and thesame or similar steps and nouns will not be repeated.

It should be understood that the various forms of processes shown abovecan be used to reorder, add or delete steps. For example, the stepsdescribed in the present application can be performed in parallel,sequentially, or in a different order, as long as the desired result ofthe technical solution disclosed in the present application can beachieved, this is not limited herein.

The foregoing specific implementations do not constitute a limitation onthe protection scope of the present application. Those skilled in theart should understand that various modifications, combinations,sub-combinations and substitutions can be made according to designrequirements and other factors. Any modifications, equivalentreplacements and improvements made within the spirit and principles ofthis application shall be included in the scope of protection of thisapplication.

What is claimed is:
 1. An intent recognition optimization processingmethod, comprising: acquiring a first intent set and at least oneoriginal corpus; acquiring a first recognition result of each originalcorpus, wherein the first recognition result of any one of originalcorpus comprises a first intent corresponding to the original corpusrecognized by an intent recognition model; acquiring a secondrecognition result of each original corpus, wherein the secondrecognition result of any one of the original corpus includes a secondintent corresponding to the original corpus obtained through artificialrecognition; and performing optimization processing on the first intentset, according to the first recognition result and the secondrecognition result of each original corpus, to obtain a second intentset.
 2. The method according to claim 1, wherein, the performingoptimization processing on the first intent set, according to the firstrecognition result and the second recognition result of each originalcorpus, to obtain a second intent set, comprises: determining, accordingto the first recognition result and the second recognition result ofeach original corpus, a first corpus number and a second corpus numbercorresponding to each intent in the first intent set respectively; andperforming optimization processing on the first intent set, according tothe first corpus number and the second corpus number corresponding toeach intent in the first intent set, to obtain a second intent set; andwherein the determining a first corpus number and a second corpus numbercorresponding to each intent in the first intent set, comprisesperforming following steps for each intent in the first intent set:determining, according to the first recognition result of each of theoriginal corpus, the number of original corpus that the first intentthereof is the one of the first intent set as the first corpus number;and determining, according to the second recognition result of each ofthe original corpus that the first intent thereof is the one of thefirst intent set, the number of original corpus that the second intentthereof is the one of the first intent set as the second corpus number.3. The method according to claim 2, wherein, the performing optimizationprocessing on the first intent set, according to the first corpus numberand the second corpus number corresponding to each intent in the firstintent set, to obtain a second intent set, comprises: determining afirst accuracy rate of the first intent set, according to the firstcorpus number and the second corpus number corresponding to each intentin the first intent set; repeating following steps, until whether everytwo intents in the first intent set need to be merged is determined:determining, according to the first corpus number and the second corpusnumber corresponding to any two intents in the first intent set, asecond accuracy rate corresponding to a third intent set obtained bymerging the two intents, and determining that the two intents need to bemerged when the second accuracy rate is higher than the first accuracyrate; and merging any two intents that need to be merged in the firstintent set, to obtain the second intent set.
 4. The method according toclaim 3, wherein, the first accuracy rate is a ratio of a sum of thesecond corpus number corresponding to all intents in the first intentset to a sum of the first corpus number corresponding to all intents inthe first intent set; and the second accuracy rate is a ratio of a sumof the second corpus number corresponding to all intents in the thirdintent set to a sum of the first corpus number corresponding to allintents in the third intent set.
 5. The method according to claim 1,wherein, the acquiring the second recognition result of each originalcorpus comprises: determining a tagging value of each original corpusaccording to the first recognition result of each original corpus, anddetermining an original corpus that the tagging value thereof exceeds aset threshold as a valuable corpus, to obtain at least one valuablecorpus; and acquiring the second recognition result of each valuablecorpus.
 6. The method according to claim 5, wherein, the firstrecognition result of each original corpus further comprises an intentconfidence of each intent in the first intent set corresponding to theoriginal corpus; and the determining, according to the first recognitionresult of each original corpus, a tagging value of each original corpus,comprises: determining, according to an intent confidence of each intentin the first intent set corresponding to the original corpus, aconfidence information entropy, a highest intent confidence and asecondary highest intent confidence corresponding to each of theoriginal corpus; determining, according to the confidence informationentropy, the highest intent confidence and the secondary highest intentconfidence corresponding to each of the original corpus, a tagging valueof each of the original corpus.
 7. The method according to claim 1,wherein, the acquiring the second recognition result of each originalcorpus comprises: determining that the second recognition result of anoriginal corpus is the same recognition result as the first recognitionresult, if the second recognition result of the original corpus is notacquired.
 8. The method according to claim 1, wherein, the methodfurther comprises selecting an intent from the second intent set toperform corpus tagging using the intent recognition model after thesecond intent set is obtained.
 9. The method according to claim 1,wherein, the method further comprises: training the intent recognitionmodel according to the second recognition result of each originalcorpus.
 10. An intent recognition optimization processing apparatus,comprising: at least one processor; and a memory communicativelyconnected with the at least one processor, wherein: the memory storesthereon instructions executable by the at least one processor, and theinstructions are executed by the at least one processor to cause the atleast one processor to implement following steps: acquiring a firstintent set and at least one original corpus; acquiring a firstrecognition result of each original corpus, wherein the firstrecognition result of any one of the original corpus comprises a firstintent corresponding to the original corpus recognized by an intentrecognition model; acquiring a second recognition result of eachoriginal corpus, wherein the second recognition result of any one of theoriginal corpus includes a second intent corresponding to the originalcorpus obtained through artificial recognition; and performingoptimization processing on the first intent set, according to the firstrecognition result and the second recognition result of each originalcorpus, to obtain a second intent set.
 11. The apparatus according toclaim 10, wherein the instructions cause the at least one processor toimplement following steps: determining a first corpus number and asecond corpus number corresponding to each intent in the first intentset according to the first recognition result and the second recognitionresult of each original corpus; and performing optimization processingon the first intent set to obtain a second intent set according to thefirst corpus number and the second corpus number corresponding to eachintent in the first intent set; and determining, according to the firstrecognition result of each of the original corpus, the number oforiginal corpus that the first intent thereof is the one of the firstintent set as the first corpus number; and determining, according to thesecond recognition result of each of the original corpus that the firstintent thereof is the one of the first intent set, the number oforiginal corpus that the second intent thereof is the one of the firstintent set as the second corpus number.
 12. The apparatus according toclaim 11, wherein the instructions cause the at least one processor toimplement following steps: determining a first accuracy rate of thefirst intent set, according to the first corpus number and the secondcorpus number corresponding to each intent in the first intent set;repeating following steps, until whether every two intents in the firstintent set need to be merged is determined: determining, according tothe first corpus number and the second corpus number corresponding toany two intents in the first intent set, a second accuracy ratecorresponding to a third intent set obtained by merging the two intents,and determining that the two intents need to be merged when the secondaccuracy rate is higher than the first accuracy rate; and merging anytwo intents that need to be merged in the first intent set, to obtainthe second intent set.
 13. The apparatus according to claim 12, wherein,the first accuracy rate is a ratio of a sum of the second corpus numbercorresponding to all intents in the first intent set to a sum of thefirst corpus number corresponding to all intents in the first intentset; and the second accuracy rate is the ratio of a sum of the secondcorpus number corresponding to all intents in the third intent set to asum of the first corpus number corresponding to all intents in the thirdintent set.
 14. The apparatus according to claim 10, wherein, theinstructions cause the at least one processor to implement followingsteps: determining a tagging value of each original corpus according tothe first recognition result of each original corpus, and determine anoriginal corpus that the tagging value thereof exceeds a set thresholdas a valuable corpus, to obtain at least one valuable corpus; andacquiring the second recognition result of each valuable corpus.
 15. Theapparatus according to claim 14, wherein, the instructions cause the atleast one processor to implement following steps: determining aconfidence information entropy, a highest intent confidence and asecondary highest intent confidence corresponding to each originalcorpus according to the intent confidence of each intent in the firstintent set corresponding to the original corpus; and determining atagging value of each original corpus according to the confidenceinformation entropy, the highest intent confidence and the secondaryhighest intent confidence corresponding to each original corpus.
 16. Theapparatus according to claim 10, wherein, the instructions cause the atleast one processor to implement following steps: determining that thesecond recognition result of an original corpus is the same taggingresult as the first recognition result, if the second recognition resultof the original corpus is not acquired.
 17. The apparatus according toclaim 10, wherein, the instructions cause the at least one processor toimplement following steps: training the intent recognition modelaccording to the second recognition result of each of the originalcorpus.
 18. The apparatus according to claim 12, wherein, theinstructions cause the at least one processor to implement followingsteps: training the intent recognition model according to the secondrecognition result of each of the original corpus.
 19. The apparatusaccording to claim 10, wherein, the instructions cause the at least oneprocessor to implement following steps: selecting an intent from thesecond intent set to perform corpus tagging using the intent recognitionmodel after the second intent set is obtained.
 20. A non-transitorycomputer-readable storage medium storing computer instructions, wherein,the computer instructions are used to cause the computer to execute themethod according to claim 1.